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Exploratory Modeling and Analysis (EMA) is a research methodology that uses
computational experiments to analyze complex and uncertain systems
(Bankes, 1993).That is,
exploratory modeling aims at offering computational decision support for
decision making under deep uncertainty
and Robust decision making.

The EMA workbench is aimed at providing support for doing EMA on models
developed in various modelling packages and environments. Currently, we focus
on offering support for doing EMA on models developed in
Vensim, Excel, and Python. Future plans include
support for Netlogo
and Repast. The EMA workbench offers support
for designing experiments, performing the experiments - including support for
parallel processing-, and analysing the results. A key design principle is that
people should be able to perform EMA on normal computers, instead of having
to take recourse to a HPC.

The Exploratory Modeling and Analysis (EMA) Workbench is an evolving set of
tools and methods. It evolved out of code written by Jan Kwakkel for his PhD
research. The EMA workbench is implemented in Python and relies on
Numpy and Scipy.

Vensim controller (vensim): This enables controlling (e.g. setting
parameters, simulation setup, run, get output, etc.) a simulation model that
is built in Vensim software, and conducting an EMA study based on this model.

Behaviour clustering (clusterer): This analysis feature automatically
allocates output behaviours that are similar in characteristics to groups
(i.e. clusters). ‘Similarity’ between dynamic behaviours is defined using
distance functions, and the feature can operate using different distance
functions that measure the (dis)similarity very differently. Currently
available distances are as follows;

Behaviour Mode Distance (distance_gonenc()): A distance that
focuses purely on qualitative pattern features. For example, two S-shaped
curves that are very different in initial level, take-off point, final
value, etc. are evaluated as identical according to BM distance since both
have identical qualitaive characteristics of an S-shaped behaviour
(i.e. a constant early phase, then growth with increasing rate, then
growth with decreasing rate and terminate with a constant late phase)
on their differences in these three features.

Exploratory Modeling and Analysis (EMA) is a research methodology that uses
computational experiments to analyze complex and uncertain systems
(Bankes, 1993, 1994). EMA can be understood as searching or sampling over an
ensemble of models that are plausible, given a priori knowledge or are
otherwise of interest. This ensemble may often be large or infinite in size.
Consequently, the central challenge of exploratory modeling is the design of
search or sampling strategies that support valid conclusions or reliable
insights based on a limited number of computational experiments.

EMA can be contrasted with the use of models to predict system behavior,
where models are built by consolidating known facts into a single package
(Hodges, 1991). When experimentally validated, this single model can be used
for analysis as a surrogate for the actual system. Examples of this approach
include the engineering models that are used in computer-aided design systems.
Where applicable, this consolidative methodology is a powerful technique for
understanding the behavior of complex systems. Unfortunately, for many systems
of interest, the construction of models that may be validly used as surrogates
is simply not a possibility. This may be due to a variety of factors, including
the infeasibility of critical experiments, impossibility of accurate
measurements or observations, immaturity of theory, openness of the system to
unpredictable outside perturbations, or nonlinearity of system behavior, but is
fundamentally a matter of not knowing enough to make predictions
(Campbell et al., 1985; Hodges and Dewar, 1992). For such systems, a
methodology based on consolidating all known information into a single model
and using it to make best estimate predictions can be highly misleading.

EMA can be useful when relevant information exists that can be exploited by
building models, but where this information is insufficient to specify a single
model that accurately describes system behavior. In this circumstance, models
can be constructed that are consistent with the available information, but such
models are not unique. Rather than specifying a single model and falsely
treating it as a reliable image of the target system, the available information
is consistent with a set of models, whose implications for potential decisions
may be quite diverse. A single model run drawn from this potentially infinite
set of plausible models is not a prediction; rather, it provides a
computational experiment that reveals how the world would behave if the
various guesses any particular model makes about the various unresolvable
uncertainties were correct. EMA is the explicit representation of the set of
plausible models, the process of exploiting the information contained in such
a set through a large number of computational experiments, and the analysis of
the results of these experiments.

A set, universe, or ensemble of models that are plausible or interesting in the
context of the research or analysis being conducted is generated by the
uncertainties associated with the problem of interest, and is constrained by
available data and knowledge. ExploratoryModelingAndAnalysis can be
viewed as a means for inference from the constraint information that specifies
this set or ensemble. Selecting a particular model out of an ensemble of
plausible ones requires making suppositions about factors that are uncertain or
unknown. One such computational experiment is typically not that informative
(beyond suggesting the plausibility of its outcomes). Instead, EMA supports
reasoning about general conclusions through the examination of the results of
numerous such experiments. Thus, EMA can be understood as search or sampling
over the ensemble of models that are plausible given a priori knowledge.